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Open Access Editorial

Grid technology in tissue-based diagnosis: fundamentals and potential developments

Jürgen Görtler1, Martin Berghoff2, Gian Kayser3 and Klaus Kayser4*

Author Affiliations

1 IBM DeepComputing, Brussels, Belgium

2 Department of Neurology, University Münster, Münster, Germany

3 Institute of Pathology, University Freiburg, Freiburg, Germany

4 UICC-TPCC, Institute of Pathology, Charite, Berlin, Germany

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Diagnostic Pathology 2006, 1:23  doi:10.1186/1746-1596-1-23

Published: 24 August 2006

Abstract

Tissue-based diagnosis still remains the most reliable and specific diagnostic medical procedure. It is involved in all technological developments in medicine and biology and incorporates tools of quite different applications. These range from molecular genetics to image acquisition and recognition algorithms (for image analysis), or from tissue culture to electronic communication services.

Grid technology seems to possess all features to efficiently target specific constellations of an individual patient in order to obtain a detailed and accurate diagnosis in providing all relevant information and references.

Grid technology can be briefly explained by so-called nodes that are linked together and share certain communication rules in using open standards. The number of nodes can vary as well as their functionality, depending on the needs of a specific user at a given point in time. In the beginning of grid technology, the nodes were used as supercomputers in combining and enhancing the computation power. At present, at least five different Grid functions can be distinguished, that comprise 1) computation services, 2) data services, 3) application services, 4) information services, and 5) knowledge services.

The general structures and functions of a Grid are described, and their potential implementation into virtual tissue-based diagnosis is analyzed. As a result Grid technology offers a new dimension to access distributed information and knowledge and to improving the quality in tissue-based diagnosis and therefore improving the medical quality.